The PARP inhibitor rucaparib blocks SARS-CoV-2 virus binding to cells and the immune reaction in models of COVID-19

\ua9 2024 The Author(s). British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.Background and Purpose: To date, there are limited options for severe Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2 virus. As ADP-ribosylation events are involved in regulating the life cycle of coronaviruses and the inflammatory reactions of the host; we have, here, assessed the repurposing of registered PARP inhibitors for the treatment of COVID-19. Experimental Approach: The effects of PARP inhibitors on virus uptake were assessed in cell-based experiments using multiple variants of SARS-CoV-2. The binding of rucaparib to spike protein was tested by molecular modelling and microcalorimetry. The anti-inflammatory properties of rucaparib were demonstrated in cell-based models upon challenging with recombinant spike protein or SARS-CoV-2 RNA vaccine. Key Results: We detected high levels of oxidative stress and strong PARylation in all cell types in the lungs of COVID-19 patients, both of which negatively correlated with lymphocytopaenia. Interestingly, rucaparib, unlike other tested PARP inhibitors, reduced the SARS-CoV-2 infection rate through binding to the conserved 493–498 amino acid region located in the spike-ACE2 interface in the spike protein and prevented viruses from binding to ACE2. In addition, the spike protein and viral RNA-induced overexpression of cytokines was down-regulated by the inhibition of PARP1 by rucaparib at pharmacologically relevant concentrations. Conclusion and Implications: These results point towards repurposing rucaparib for treating inflammatory responses in COVID-19

Free energies of binding of R- and S-propranolol to wild-type and F483A mutant cytochrome P450 2D6 from molecular dynamics simulations

Detailed molecular dynamics (MD) simulations have been performed to reproduce and rationalize the experimental finding that the F483A mutant of CYP2D6 has lower affinity for R-propranolol than for S-propranolol. Wild-type (WT) CYP2D6 does not show this stereospecificity. Four different approaches to calculate the free energy differences have been investigated and were compared to the experimental binding data. From the differences between calculations based on forward and backward processes and the closure of thermodynamic cycles, it was clear that not all simulations converged sufficiently. The approach that calculates the free energies of exchanging R-propranolol with S-propranolol in the F483A mutant relative to the exchange free energy in WT CYP2D6 accurately reproduced the experimental binding data. Careful inspection of the end-points of the MD simulations involved in this approach, allowed for a molecular interpretation of the observed differences

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%